STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction - Zhihong Chang; Chunsheng Liu; Jianmin Jia (Applied Sciences, 2023)
As an important component of intelligent transportation-management systems, accurate traffic-parameter prediction can help traffic-management departments to conduct effective traffic management. Due to the nonlinearity, complexity, and dynamism of highway-traffic data, traffic-flow prediction is still a challenging issue. Currently, most spatial–temporal traffic-flow-prediction models adopt fixed-structure time convolutional and graph convolutional models, which lack the ability to capture the dynamic characteristics of traffic flow. To address this issue, this paper proposes a spatial–temporal prediction model that can capture the dynamic spatial–temporal characteristics of traffic flow, named the spatial–temporal self-attention graph convolutional network (STA-GCN). In terms of feature engineering, we used the time cosine decomposition and one-hot encoding methods to capture the periodicity and heterogeneity of traffic-flow changes. Additionally, in order to build the model, self-attention mechanisms were incorporated into the spatial–temporal convolution to capture the spatial–temporal dynamic characteristics of traffic flow. The experimental results indicate that the performance of the proposed model on two traffic-volume datasets is superior to those of several baseline models. In particular, in long-term prediction, the prediction error can be reduced by over 5%. Further, the interpretability and robustness of the prediction model are addressed by considering the spatial dynamic changes.